BqExAvI Survey Analysis (2017-2022)
Modesto
2023-03-01
Moodle survey
We are importing the data from the students answers in 5 years, between 2018-2022. The quiz consists on up to 77 questions, being 5 free-text questions (50, 51, 52, 75, 76 & 77), 7 three-options questions and the rest in Likert scale. The latter will be categorized as “Positive” or “Negative” if 5 (“Totally agree”) means a “good” or “bad” feedback.
#load questions
questions <- read.csv("questions.csv", head=TRUE, sep=";")
questions <- cbind(row.names(questions),questions[,c(3,1,2)])
#add type variable
questions$type <- "Pos."
questions$type[questions$Section=="Open"] <- ""
questions[c(4,10,56,63,63,64,65,66,67,72),5] <- "Neg."
questions$type[questions$Section=="Open"] <- "NA"
colnames(questions) <- c("No.","Since","Section","Question","Type")
kbl(questions, align = "cccl", caption = "Table 1. Students opinion quizz. The bulk of the questionaire was designed for the year 2017 and new questions were added as indicated in the rightmost column.") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
column_spec(1, italic = T)| No. | Since | Section | Question | Type |
|---|---|---|---|---|
| 1 | 2017-2018 | Equipment | We had sufficient amount of small lab equipment (e.g. pippettes, cuvettes,…) | Pos. |
| 2 | 2017-2018 | Equipment | Access to general instruments/equipment (e.g. PCR, laminar flow hoods) was limiting | Pos. |
| 3 | 2017-2018 | Equipment | Lab equipment was well mantained/modern | Pos. |
| 4 | 2017-2018 | Length and schedule | The course is too long | Neg. |
| 5 | 2017-2018 | Length and schedule | The period allocated for the course within the term is appropriate | Pos. |
| 6 | 2017-2018 | Length and schedule | On average, the number of hours per day is sufficient | Pos. |
| 7 | 2017-2018 | General Methodology | Student engagement | Pos. |
| 8 | 2017-2018 | General Methodology | Course interest | Pos. |
| 9 | 2017-2018 | General Methodology | Course usefulness | Pos. |
| 10 | 2017-2018 | General Methodology | Demand on student’s part | Neg. |
| 11 | 2017-2018 | General Methodology | Course difficulty | Pos. |
| 12 | 2017-2018 | Method objectives | State a hypothesis | Pos. |
| 13 | 2017-2018 | Method objectives | Design an experimental strategy to answer specific biological questions | Pos. |
| 14 | 2017-2018 | Method objectives | Identify cost-effective (time and money-wise) practical techniques to answer a given question | Pos. |
| 15 | 2017-2018 | Method objectives | Write an experimental protocol including all relevant controls | Pos. |
| 16 | 2017-2018 | Method objectives | Execute an experimental protocol | Pos. |
| 17 | 2017-2018 | Method objectives | Record, analyze and interpret experimental results | Pos. |
| 18 | 2017-2018 | Method objectives | Integrate information from other researchers and discuss your results within a larger context | Pos. |
| 19 | 2017-2018 | Method objectives | Follow lab safety rules | Pos. |
| 20 | 2017-2018 | Method objectives | Ability to work in a team | Pos. |
| 21 | 2017-2018 | Activities interest | Introductory lecture (day 1) | Pos. |
| 22 | 2017-2018 | Activities interest | Stats Intro (day 2) | Pos. |
| 23 | 2017-2018 | Activities interest | Initial brainstorming session (day 2) | Pos. |
| 24 | 2017-2018 | Activities interest | Group meetings (experiment planning and design) | Pos. |
| 25 | 2017-2018 | Activities interest | Team feed-back session (propossal design) | Pos. |
| 26 | 2017-2018 | Activities interest | Final group discussion session (day before the last) | Pos. |
| 27 | 2017-2018 | Activities interest | Final class discussion session (last day) | Pos. |
| 28 | 2017-2018 | Activities Length | Introductory lecture (day 1) | Pos. |
| 29 | 2017-2018 | Activities Length | Stats Intro (day 2) | Pos. |
| 30 | 2017-2018 | Activities Length | Initial brainstorming session (day 2) | Pos. |
| 31 | 2017-2018 | Activities Length | Group meetings (experiment planning and design) | Pos. |
| 32 | 2017-2018 | Activities Length | Team feed-back sessions | Pos. |
| 33 | 2017-2018 | Activities Length | Final group discussion session (day before the last) | Pos. |
| 34 | 2017-2018 | Activities Length | Final class discussion session (last day) | Pos. |
| 35 | 2017-2018 | Assesment | Safety and clean-up weekly evaluation | Pos. |
| 36 | 2017-2018 | Assesment | Individual lab notebook evaluation (design) | Pos. |
| 37 | 2017-2018 | Assesment | Group lab notebook evaluation (design) | Pos. |
| 38 | 2017-2018 | Assesment | Group lab notebook evaluation (results) | Pos. |
| 39 | 2017-2018 | Assesment | Peer-evaluation (Team) | Pos. |
| 40 | 2017-2018 | Assesment | Final exam | Pos. |
| 41 | 2017-2018 | Learning Objectives | State a hypothesis | Pos. |
| 42 | 2017-2018 | Learning Objectives | Design an experimental strategy to answer specific biological questions | Pos. |
| 43 | 2017-2018 | Learning Objectives | Identify cost-effective (time and money-wise) practical techniques to answer a given question | Pos. |
| 44 | 2017-2018 | Learning Objectives | Write an experimental protocol including all relevant controls | Pos. |
| 45 | 2017-2018 | Learning Objectives | Execute an experimental protocol | Pos. |
| 46 | 2017-2018 | Learning Objectives | Record, analyze and interpret experimental results | Pos. |
| 47 | 2017-2018 | Learning Objectives | Integrate information from other researchers and discuss your results within a larger context | Pos. |
| 48 | 2017-2018 | Learning Objectives | Follow lab safety rules | Pos. |
| 49 | 2017-2018 | Learning Objectives | Ability to work in a team | Pos. |
| 50 | 2017-2018 | Open | Best | NA |
| 51 | 2017-2018 | Open | Wors | NA |
| 52 | 2019-2020 | Open | Open comments | NA |
| 53 | 2019-2020 | ELN | I knew OneNote before the course. | Pos. |
| 54 | 2019-2020 | ELN | I find OneNote a useful app for the elaboration of the course Notebook. | Pos. |
| 55 | 2019-2020 | ELN | I think we had enough previous information about the use of OneNote for the course laboratory notebook. | Pos. |
| 56 | 2019-2020 | ELN | I would prefer other applications like Evernote, Google Docs, or MS Word. | Neg. |
| 57 | 2019-2020 | ELN | The structure of the Notebook in Sections, Pages, and Subpages was easy to understand and use. | Pos. |
| 58 | 2019-2020 | ELN | I would recommend to use OneNote for the ELN next year. | Pos. |
| 59 | 2019-2020 | ELN | It overall allowed us to save time through the whole course. | Pos. |
| 60 | 2019-2020 | ELN | It helped us in the preparation of the final version to be presented. | Pos. |
| 61 | 2019-2020 | ELN | It made it easier working in groups. | Pos. |
| 62 | 2019-2020 | ELN | It could be useful for other practical courses. | Pos. |
| 63 | 2019-2020 | ELN | Synchronization issues hindered our teamworking. | Neg. |
| 64 | 2019-2020 | ELN | Conflicts of versions were very frequent. | Neg. |
| 65 | 2019-2020 | ELN | Access to the ELN was slow. | Neg. |
| 66 | 2019-2020 | ELN | We missed several wordprocessing features. | Neg. |
| 67 | 2019-2020 | ELN | Some features of the application did not work properly. | Neg. |
| 68 | 2019-2020 | ELN | The template helped us to prepare the final version of the notebook. | Pos. |
| 69 | 2019-2020 | ELN | The structure of the template matches the evaluation rubric. | Pos. |
| 70 | 2019-2020 | ELN | The structure of 4 independent notes helped to understand each step of the work in a research lab. | Pos. |
| 71 | 2019-2020 | ELN | The word limit in some sections of the template is appropriate (1 | Pos. |
| 72 | 2019-2020 | ELN | We had to modify some sections of the template to suit our experimental work. | Neg. |
| 73 | 2019-2020 | ELN | The template will facilitate an even evaluation. | Pos. |
| 74 | 2019-2020 | ELN | The use of an ELN with OneNote in this course will help us in future laboratory work (TFG, TFM, etc.). | Pos. |
| 75 | 2021-2022 | Open | Open comments ELN | NA |
| 76 | 2021-2022 | Open | Positive aspects about the project desing teams session | NA |
| 77 | 2021-2022 | Open | Negative aspects about the project desing teams session | NA |
Load data and pairwise t.test
Survey answers were downloaded from Moodle as txt/csv files. Moodle updates generated some format differences that could be bypassed after opening the files with Numbers and exporting as tables with “;” as column separator. We now show pairwise t.tests carried out to identify significant differences per year in the answers.
#read the data in a list of dataframes
#didn't use the headers to avoid mistakes
quiz <- lapply(2017:2022, function(x) read.csv(paste0("survey",x,".csv"),header=FALSE,skip=1,sep=";"))
#add Year as the third variable (empty so far)
curso <- c("2017","2018","2019","2020","2021","2022")
for (i in 1:length(quiz)){
quiz[[i]][,3] <- curso[i]
}
#adjust questions changes
#remove questions in column 32 & 40 from 2017, because we removed it in the following years
quiz[[1]] <- quiz[[1]][,-c(32,40)]
quiz[[6]] <- quiz[[6]][,-10]
names(quiz[[6]]) <- names(quiz[[5]])
names(quiz[[1]]) <- names(quiz[[2]])
#merge all dataframes and name the columns
data <- Reduce(function(x, y) merge(x, y, all=TRUE), quiz)
#take the colnames from the last quiz that contains all the questions
colnames(data)[3] <- "Curso"
#statistics analysis
#subset questions 1: remove leftmost junk columns
subdata <- data[,c(3,11:87)]
names(subdata) <- c("Curso",paste0("Q",1:77))
#subset questions 2: remove open questions
open <- c(row.names(questions[questions$Section=="Open",]))
subdata <- subdata[,-(as.integer(open)+1)]
subdata <- sapply(subdata,as.numeric)
subdata <- as.data.frame(subdata)
tests <- list()
nombres <- c()
for (i in 2:ncol(subdata)){
subdata[,i][!(subdata[,i] %in% c(1,2,3,4,5))] <- NA
#subset for years with answers to avoid void groups
kkk <- subset(subdata,!is.na(subdata[,i]))
tests[[i-1]] <- pairwise.t.test(x=as.numeric(kkk[,i]),g=as.numeric(kkk[,1]),paired = F)
nombres[i-1] <- questions[,4][(which(questions$No. %in% gsub("\\D", "",colnames(subdata[i]))))]
names(tests) <- nombres
print(names(tests[i-1]))
print(tests[[i-1]]$p.value)
}## [1] "We had sufficient amount of small lab equipment (e.g. pippettes, cuvettes,...)"
## 2017 2018 2019 2020 2021
## 2018 4.835902e-05 NA NA NA NA
## 2019 3.879170e-02 0.29237424 NA NA NA
## 2020 4.884986e-07 1.00000000 0.0419191 NA NA
## 2021 2.620952e-01 0.05789424 1.0000000 0.003876997 NA
## 2022 4.191910e-02 0.45476018 1.0000000 0.092183903 1
## [1] "Access to general instruments/equipment (e.g. PCR, laminar flow hoods) was limiting"
## 2017 2018 2019 2020 2021
## 2018 1.0000000 NA NA NA NA
## 2019 1.0000000 1 NA NA NA
## 2020 0.8109768 1 1 NA NA
## 2021 1.0000000 1 1 0.8344372 NA
## 2022 1.0000000 1 1 1.0000000 1
## [1] "Lab equipment was well mantained/modern"
## 2017 2018 2019 2020 2021
## 2018 0.4899485 NA NA NA NA
## 2019 1.0000000 1 NA NA NA
## 2020 1.0000000 1 1 NA NA
## 2021 1.0000000 1 1 1 NA
## 2022 0.6531043 1 1 1 1
## [1] "The course is too long"
## 2017 2018 2019 2020 2021
## 2018 0.0026643833 NA NA NA NA
## 2019 0.0004996605 1 NA NA NA
## 2020 0.1019667926 1 1 NA NA
## 2021 0.0868213996 1 1 1 NA
## 2022 0.1248722514 1 1 1 1
## [1] "The period allocated for the course within the term is appropriate"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "On average, the number of hours per day is sufficient"
## 2017 2018 2019 2020 2021
## 2018 0.0001740842 NA NA NA NA
## 2019 0.7083631075 0.0082785316 NA NA NA
## 2020 1.0000000000 0.0000373255 0.557747775 NA NA
## 2021 0.1093921851 0.2837206178 0.699933496 3.884956e-02 NA
## 2022 0.0002382551 1.0000000000 0.009910691 5.433618e-05 0.2837206
## [1] "Student engagement"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1.0000000 NA NA NA
## 2020 1 0.2189352 0.3199053 NA NA
## 2021 1 1.0000000 1.0000000 1.0000000 NA
## 2022 1 1.0000000 1.0000000 0.1269859 1
## [1] "Course interest"
## 2017 2018 2019 2020 2021
## 2018 1.0000000 NA NA NA NA
## 2019 1.0000000 1 NA NA NA
## 2020 0.2988613 1 1 NA NA
## 2021 1.0000000 1 1 0.1231708 NA
## 2022 1.0000000 1 1 0.3158588 1
## [1] "Course usefulness"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1.0000000 NA NA NA
## 2020 1 0.3070813 0.401915 NA NA
## 2021 1 1.0000000 1.000000 0.2706641 NA
## 2022 1 1.0000000 1.000000 1.0000000 1
## [1] "Demand on student's part"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "Course difficulty"
## 2017 2018 2019 2020 2021
## 2018 1.0000000 NA NA NA NA
## 2019 1.0000000 1.0000000 NA NA NA
## 2020 0.5995961 0.2087174 0.752361 NA NA
## 2021 1.0000000 1.0000000 1.000000 1.00000000 NA
## 2022 1.0000000 1.0000000 1.000000 0.09488378 1
## [1] "State a hypothesis"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 0.5792492 NA
## 2022 1 1 1 1.0000000 1
## [1] "Design an experimental strategy to answer specific biological questions"
## 2017 2018 2019 2020 2021
## 2018 0.9819431 NA NA NA NA
## 2019 1.0000000 1 NA NA NA
## 2020 1.0000000 1 1 NA NA
## 2021 1.0000000 1 1 1 NA
## 2022 0.9819431 1 1 1 1
## [1] "Identify cost-effective (time and money-wise) practical techniques to answer a given question"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "Write an experimental protocol including all relevant controls"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 0.8385782 NA NA
## 2021 1 1 0.9188235 1 NA
## 2022 1 1 1.0000000 1 1
## [1] "Execute an experimental protocol"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "Record, analyze and interpret experimental results"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1.0000000 NA NA NA
## 2020 1 0.6303827 1 NA NA
## 2021 1 1.0000000 1 0.67234486 NA
## 2022 1 1.0000000 1 0.07443221 1
## [1] "Integrate information from other researchers and discuss your results within a larger context"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "Follow lab safety rules"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 0.1591204 NA NA NA
## 2020 1 0.1062739 1 NA NA
## 2021 1 1.0000000 1 1 NA
## 2022 1 1.0000000 1 1 1
## [1] "Ability to work in a team"
## 2017 2018 2019 2020 2021
## 2018 1.0000000 NA NA NA NA
## 2019 0.1609205 1 NA NA NA
## 2020 1.0000000 1 0.7693568 NA NA
## 2021 1.0000000 1 0.9866925 1 NA
## 2022 0.4842893 1 1.0000000 1 1
## [1] "Introductory lecture (day 1)"
## 2017 2018 2019 2020 2021
## 2018 1.0000000 NA NA NA NA
## 2019 1.0000000 1.0000000 NA NA NA
## 2020 1.0000000 1.0000000 1.0000000 NA NA
## 2021 1.0000000 1.0000000 1.0000000 1 NA
## 2022 0.1719199 0.2035414 0.2110407 1 0.4244263
## [1] "Stats Intro (day 2)"
## 2017 2018 2019 2020 2021
## 2018 9.016649e-02 NA NA NA NA
## 2019 8.152269e-05 0.7431196 NA NA NA
## 2020 1.632072e-04 0.7465066 1 NA NA
## 2021 8.674063e-05 0.7431196 1 1 NA
## 2022 8.588806e-04 1.0000000 1 1 1
## [1] "Initial brainstorming session (day 2)"
## 2017 2018 2019 2020 2021
## 2018 0.7350214 NA NA NA NA
## 2019 1.0000000 1.00000000 NA NA NA
## 2020 1.0000000 0.54883014 1.0000000 NA NA
## 2021 1.0000000 0.08910869 1.0000000 1 NA
## 2022 1.0000000 0.02113266 0.4213264 1 1
## [1] "Group meetings (experiment planning and design)"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1.0000000 NA NA
## 2021 1 1 1.0000000 1.0000000 NA
## 2022 1 1 0.4134602 0.4928046 1
## [1] "Team feed-back session (propossal design)"
## 2017 2018 2019 2020 2021
## 2018 1.000000e+00 NA NA NA NA
## 2019 1.000000e+00 1.000000e+00 NA NA NA
## 2020 1.000000e+00 1.000000e+00 1.000000e+00 NA NA
## 2021 5.122069e-05 9.484981e-08 1.323700e-06 9.133855e-08 NA
## 2022 1.580189e-03 9.221399e-06 8.708906e-05 9.221399e-06 1
## [1] "Final group discussion session (day before the last)"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1.0000000 NA NA NA
## 2020 1 1.0000000 1.0000000 NA NA
## 2021 1 0.8147184 0.8058628 1 NA
## 2022 1 1.0000000 1.0000000 1 1
## [1] "Final class discussion session (last day)"
## 2017 2018 2019 2020 2021
## 2018 1.00000000 NA NA NA NA
## 2019 0.05752352 1 NA NA NA
## 2020 1.00000000 1 0.05752352 NA NA
## 2021 1.00000000 1 0.19394869 1 NA
## 2022 1.00000000 1 0.13401895 1 1
## [1] "Introductory lecture (day 1)"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "Stats Intro (day 2)"
## 2017 2018 2019 2020 2021
## 2018 7.920217e-05 NA NA NA NA
## 2019 1.121074e-02 0.923398 NA NA NA
## 2020 4.812777e-05 1.000000 0.9231893 NA NA
## 2021 4.677896e-03 1.000000 1.0000000 1 NA
## 2022 5.872309e-06 1.000000 0.3164371 1 0.6849254
## [1] "Initial brainstorming session (day 2)"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1.0000000 NA NA NA
## 2020 1 1.0000000 1 NA NA
## 2021 1 0.9746626 1 1 NA
## 2022 1 0.4920238 1 1 1
## [1] "Group meetings (experiment planning and design)"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 0.5996380 NA NA NA
## 2020 1 1.0000000 1 NA NA
## 2021 1 1.0000000 1 1.0000000 NA
## 2022 1 0.1165861 1 0.4314979 0.9808853
## [1] "Team feed-back sessions"
## 2017 2018 2019 2020 2021
## 2018 1.000000e+00 NA NA NA NA
## 2019 1.000000e+00 1.000000e+00 NA NA NA
## 2020 1.000000e+00 1.000000e+00 1.000000e+00 NA NA
## 2021 3.883002e-05 2.066388e-04 2.745649e-03 2.745649e-03 NA
## 2022 9.589759e-09 9.147903e-08 1.842478e-06 2.020658e-06 0.4156231
## [1] "Final group discussion session (day before the last)"
## 2017 2018 2019 2020 2021
## 2018 1.0000000 NA NA NA NA
## 2019 1.0000000 1 NA NA NA
## 2020 1.0000000 1 1.0000000 NA NA
## 2021 1.0000000 1 0.5053068 0.8400922 NA
## 2022 0.9660558 1 0.4379554 0.7278093 1
## [1] "Final class discussion session (last day)"
## 2017 2018 2019 2020 2021
## 2018 0.01265194 NA NA NA NA
## 2019 0.12572353 1.00000000 NA NA NA
## 2020 1.00000000 0.00403720 0.04786672 NA NA
## 2021 1.00000000 0.02036425 0.18483993 1 NA
## 2022 1.00000000 0.22708031 1.00000000 1 1
## [1] "Safety and clean-up weekly evaluation"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "Individual lab notebook evaluation (design)"
## 2017 2018 2019 2020 2021
## 2018 5.986044e-06 NA NA NA NA
## 2019 3.624199e-02 0.1515468 NA NA NA
## 2020 3.708189e-03 0.9621303 1 NA NA
## 2021 4.132923e-03 0.8032681 1 1 NA
## 2022 2.823163e-03 1.0000000 1 1 1
## [1] "Group lab notebook evaluation (design)"
## 2017 2018 2019 2020 2021
## 2018 0.009839814 NA NA NA NA
## 2019 0.030480248 1.0000000 NA NA NA
## 2020 0.326865132 1.0000000 1 NA NA
## 2021 0.862805649 0.8628056 1 1 NA
## 2022 1.000000000 0.5820280 1 1 1
## [1] "Group lab notebook evaluation (results)"
## 2017 2018 2019 2020 2021
## 2018 0.7761493 NA NA NA NA
## 2019 1.0000000 0.85014326 NA NA NA
## 2020 1.0000000 0.08987183 1.0000000 NA NA
## 2021 1.0000000 0.11606382 1.0000000 1 NA
## 2022 1.0000000 0.02078403 0.9301167 1 1
## [1] "Peer-evaluation (Team)"
## 2017 2018 2019 2020 2021
## 2018 0.2010087 NA NA NA NA
## 2019 0.9374738 1.0000000 NA NA NA
## 2020 1.0000000 1.0000000 1 NA NA
## 2021 1.0000000 0.4252861 1 1 NA
## 2022 1.0000000 1.0000000 1 1 1
## [1] "Final exam"
## 2017 2018 2019 2020 2021
## 2018 1.00000000 NA NA NA NA
## 2019 1.00000000 1.0000000 NA NA NA
## 2020 1.00000000 1.0000000 1.000000000 NA NA
## 2021 0.19443595 0.1119257 0.588829367 0.19443595 NA
## 2022 0.04751644 0.2425330 0.008068794 0.06184887 1.291026e-05
## [1] "State a hypothesis"
## 2017 2018 2019 2020 2021
## 2018 1.00000000 NA NA NA NA
## 2019 0.04850727 0.04850727 NA NA NA
## 2020 1.00000000 1.00000000 0.2405459 NA NA
## 2021 0.17406452 0.17406452 1.0000000 0.6417639 NA
## 2022 0.10429512 0.10429512 1.0000000 0.3946902 1
## [1] "Design an experimental strategy to answer specific biological questions"
## 2017 2018 2019 2020 2021
## 2018 0.2256925 NA NA NA NA
## 2019 1.0000000 1.00000000 NA NA NA
## 2020 1.0000000 0.11316070 1 NA NA
## 2021 1.0000000 0.05348894 1 1 NA
## 2022 1.0000000 1.00000000 1 1 0.6999663
## [1] "Identify cost-effective (time and money-wise) practical techniques to answer a given question"
## 2017 2018 2019 2020 2021
## 2018 0.8223218 NA NA NA NA
## 2019 1.0000000 0.1174194 NA NA NA
## 2020 1.0000000 0.8007973 1.0000000 NA NA
## 2021 1.0000000 1.0000000 0.8007973 1 NA
## 2022 1.0000000 1.0000000 1.0000000 1 1
## [1] "Write an experimental protocol including all relevant controls"
## 2017 2018 2019 2020 2021
## 2018 0.05017013 NA NA NA NA
## 2019 1.00000000 0.11678843 NA NA NA
## 2020 1.00000000 0.03687698 1 NA NA
## 2021 1.00000000 0.22014254 1 1 NA
## 2022 1.00000000 0.71000111 1 1 1
## [1] "Execute an experimental protocol"
## 2017 2018 2019 2020 2021
## 2018 0.06715688 NA NA NA NA
## 2019 1.00000000 1.0000000 NA NA NA
## 2020 1.00000000 0.5912804 1 NA NA
## 2021 1.00000000 0.2934492 1 1 NA
## 2022 1.00000000 0.5927727 1 1 1
## [1] "Record, analyze and interpret experimental results"
## 2017 2018 2019 2020 2021
## 2018 0.05965194 NA NA NA NA
## 2019 1.00000000 0.17943892 NA NA NA
## 2020 1.00000000 0.08604197 1 NA NA
## 2021 1.00000000 0.38331922 1 1.0000000 NA
## 2022 0.57856745 1.00000000 1 0.7137619 1
## [1] "Integrate information from other researchers and discuss your results within a larger context"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 1 NA NA NA
## 2020 1 1 1 NA NA
## 2021 1 1 1 1 NA
## 2022 1 1 1 1 1
## [1] "Follow lab safety rules"
## 2017 2018 2019 2020 2021
## 2018 1 NA NA NA NA
## 2019 1 0.3584685 NA NA NA
## 2020 1 1.0000000 1 NA NA
## 2021 1 1.0000000 1 1 NA
## 2022 1 0.0763971 1 1 1
## [1] "Ability to work in a team"
## 2017 2018 2019 2020 2021
## 2018 0.5300693 NA NA NA NA
## 2019 1.0000000 1.0000000 NA NA NA
## 2020 1.0000000 0.9486344 1 NA NA
## 2021 1.0000000 1.0000000 1 1 NA
## 2022 1.0000000 1.0000000 1 1 1
## [1] "I knew OneNote before the course."
## 2019 2020 2021
## 2020 1.000000000 NA NA
## 2021 1.000000000 1.00000000 NA
## 2022 0.001084163 0.01673493 0.01673493
## [1] "I find OneNote a useful app for the elaboration of the course Notebook."
## 2019 2020 2021
## 2020 0.0194452 NA NA
## 2021 0.4290317 0.4290317085 NA
## 2022 0.4290317 0.0009279188 0.05334355
## [1] "I think we had enough previous information about the use of OneNote for the course laboratory notebook."
## 2019 2020 2021
## 2020 0.9032147 NA NA
## 2021 0.2647527 0.08479041 NA
## 2022 0.2647527 0.08179045 0.9032147
## [1] "I would prefer other applications like Evernote, Google Docs, or MS Word."
## 2019 2020 2021
## 2020 0.350094298 NA NA
## 2021 0.285699438 7.806553e-01 NA
## 2022 0.000694997 7.056524e-06 1.613306e-06
## [1] "The structure of the Notebook in Sections, Pages, and Subpages was easy to understand and use."
## 2019 2020 2021
## 2020 1 NA NA
## 2021 1 1.0000000 NA
## 2022 1 0.9442328 0.9442328
## [1] "I would recommend to use OneNote for the ELN next year."
## 2019 2020 2021
## 2020 0.002215487 NA NA
## 2021 0.172920608 1.962236e-01 NA
## 2022 0.228618042 6.958609e-05 0.01373391
## [1] "It overall allowed us to save time through the whole course."
## 2019 2020 2021
## 2020 0.3052495 NA NA
## 2021 0.7158066 0.54233771 NA
## 2022 0.5564205 0.03337407 0.5423377
## [1] "It helped us in the preparation of the final version to be presented."
## 2019 2020 2021
## 2020 0.1701509 NA NA
## 2021 0.1701509 0.9558176 NA
## 2022 0.7062676 0.0374604 0.03256321
## [1] "It made it easier working in groups."
## 2019 2020 2021
## 2020 0.01181064 NA NA
## 2021 0.50976729 5.372195e-02 NA
## 2022 0.10713885 2.366656e-05 0.04984517
## [1] "It could be useful for other practical courses."
## 2019 2020 2021
## 2020 0.09691404 NA NA
## 2021 0.55929640 0.598200546 NA
## 2022 0.59820055 0.009901279 0.1035011
## [1] "Synchronization issues hindered our teamworking."
## 2019 2020 2021
## 2020 1.00000000 NA NA
## 2021 1.00000000 1.0000000 NA
## 2022 0.06488059 0.2322175 0.06488059
## [1] "Conflicts of versions were very frequent."
## 2019 2020 2021
## 2020 0.74604855 NA NA
## 2021 0.74604855 0.9710615 NA
## 2022 0.03894251 0.5201819 0.5201819
## [1] "Access to the ELN was slow."
## 2019 2020 2021
## 2020 0.2325937 NA NA
## 2021 0.7892961 0.02082084 NA
## 2022 1.0000000 0.10289719 1
## [1] "We missed several wordprocessing features."
## 2019 2020 2021
## 2020 1 NA NA
## 2021 1 1 NA
## 2022 1 1 1
## [1] "Some features of the application did not work properly."
## 2019 2020 2021
## 2020 1.00000000 NA NA
## 2021 0.11460584 0.1956151 NA
## 2022 0.05474198 0.1146058 1
## [1] "The template helped us to prepare the final version of the notebook."
## 2019 2020 2021
## 2020 7.618311e-01 NA NA
## 2021 1.688823e-05 0.0004018526 NA
## 2022 3.999242e-01 0.7618310929 0.01161285
## [1] "The structure of the template matches the evaluation rubric."
## 2019 2020 2021
## 2020 1.0000000000 NA NA
## 2021 0.0001986663 0.0009679592 NA
## 2022 1.0000000000 1.0000000000 0.006709094
## [1] "The structure of 4 independent notes helped to understand each step of the work in a research lab."
## 2019 2020 2021
## 2020 0.7663575 NA NA
## 2021 0.2501597 0.9376949 NA
## 2022 0.9376949 0.7663575 0.2501597
## [1] "The word limit in some sections of the template is appropriate (1"
## 2019 2020 2021
## 2020 1 NA NA
## 2021 1 1 NA
## 2022 1 1 1
## [1] "We had to modify some sections of the template to suit our experimental work."
## 2019 2020 2021
## 2020 3.220546e-06 NA NA
## 2021 1.115448e-06 0.8425691 NA
## 2022 1.868463e-03 0.3594123 0.3594123
## [1] "The template will facilitate an even evaluation."
## 2019 2020 2021
## 2020 9.979675e-01 NA NA
## 2021 6.709339e-05 0.003506002 NA
## 2022 1.000000e+00 1.000000000 0.001176378
## [1] "The use of an ELN with OneNote in this course will help us in future laboratory work (TFG, TFM, etc.)."
## 2019 2020 2021
## 2020 0.4321188 NA NA
## 2021 0.4232123 1.0000000 NA
## 2022 1.0000000 0.4232123 0.3318217Boxplots
Answers from each question are displayed in Boxplots by Year and significant differences from the overall answers are marked.
#update question nubers
#stack table and add question type (except open)
subdata.stack <- cbind(subdata[,1],stack(subdata[,2:72]))
names(subdata.stack) <- c("Curso","value","question")
#add categories
for (i in 1:nrow(subdata.stack)){
subdata.stack[i,4] <- questions$Section[subdata.stack[i,3]==paste0("Q",questions$No.)]
}
names(subdata.stack) <- c("Curso","value","question","section")
pp <- list()
plot <- list()
#plot in groups by section
for (i in 1:length(levels(as.factor(subdata.stack$section)))){
kkk <- subdata.stack[subdata.stack$section==levels(as.factor(subdata.stack$section))[i],]
pp[[i]] <- ggboxplot(kkk, x = "Curso", y = "value",
color = "Curso", fill = "Curso",palette = "jco",
add = "jitter",alpha = 0.3, ylim = c(0, 6),
facet.by = "question")
plot[[i]] <- pp[[i]] + ggtitle(levels(as.factor(subdata.stack$section))[i]) + theme(axis.text.x = element_text(size = 15, angle=45,vjust=0.7),axis.text.y = element_text(size = 15),plot.title=element_text(hjust=0.5,face="italic")) + theme(legend.position="none") +
stat_compare_means(label = "p.signif",method = "t.test", ref.group = ".all.", hide.ns = TRUE,label.y=5.5,size=6)
}plot[[1]]
plot[[2]]
plot[[3]]
plot[[4]]
plot[[5]]
plot[[6]]
plot[[7]]
plot[[8]]
plot[[9]]
Likert scale plots
Answers in Likert scales are represented by sections in the quiz.
General Methodology
#lickert
#change question names
tablita <- data.frame(matrix(NA, # Create empty data frame
nrow = length(colnames(subdata)),
ncol = 2))
for (i in 2:length(colnames(subdata))){
tablita[i-1,] <- cbind(colnames(subdata[i]),questions$Question[as.numeric(gsub("\\D", "",colnames(subdata[i])))])
colnames(subdata)[i] <- tablita[i-1,2]
}
for (i in 2:72){
subdata[,i] <- factor(subdata[,i])
}
#questions with 3 options
#General Methodology
subdata[,c(8:12)] <- lapply(subdata[,c(8:12)], function(x) factor(x,
labels = c("Traditional","Same","Open Question"))
)
xlikgroup3a = likert(subdata[,c(8:12)], grouping = subdata$Curso)
plot(xlikgroup3a, type = "bar", centered = T) 
Activities Length
subdata[,c(29:35)] <- lapply(subdata[,c(29:35)], function(x) factor(x,
labels = c("Less","Fine","More"))
)
xlikgroup3b = likert(subdata[,c(29:35)], grouping = subdata$Curso)
plot(xlikgroup3b, type = "bar", centered = T)
#title(main = "Activities Length", xlab = "X axis", ylab = "Y axis", cex.main = 4, font.main = 3)
#legend("bottom", c("Less","Fine","More"))Equipment
subdata[,c(2:4)] <- lapply(subdata[,c(2:4)], function(x) factor(x,
labels = c("Not at all","Disagree","OK","Agree","Completely Agree"))
)
xlikgroup5a = likert(subdata[,c(2:4)], grouping = subdata$Curso)
plot(xlikgroup5a, type = "bar", centered = T)
Length and Schedule
subdata[,c(5:7)] <- lapply(subdata[,c(5:7)], function(x) factor(x,
labels = c("Not at all","Disagree","OK","Agree","Completely Agree"))
)
xlikgroup5b = likert(subdata[,c(5:7)], grouping = subdata$Curso)
plot(xlikgroup5b, type = "bar", centered = T)
Method Objectives
subdata[,c(13:21)] <- lapply(subdata[,c(13:21)], function(x) factor(x,
labels = c("Not at all","Disagree","OK","Agree","Completely Agree"))
)
xlikgroup5c = likert(subdata[,c(13:21)], grouping = subdata$Curso)
plot(xlikgroup5c, type = "bar", centered = T)
Activities Interest
subdata[,c(22:28)] <- lapply(subdata[,c(22:28)], function(x) factor(x,
labels = c("Not at all","Disagree","OK","Agree","Completely Agree"))
)
xlikgroup5d = likert(subdata[,c(22:28)], grouping = subdata$Curso)
plot(xlikgroup5d, type = "bar", centered = T)
Assessment
subdata[,c(36:41)] <- lapply(subdata[,c(36:41)], function(x) factor(x,
labels = c("Not at all","Disagree","OK","Agree","Completely Agree"))
)
xlikgroup5e = likert(subdata[,c(36:41)], grouping = subdata$Curso)
plot(xlikgroup5e, type = "bar", centered = T)
Learning Objectives
subdata[,c(43:50)] <- lapply(subdata[,c(42:50)], function(x) factor(x,
labels = c("Not at all","Disagree","OK","Agree","Completely Agree"))
)
xlikgroup5f = likert(subdata[,c(42:50)], grouping = subdata$Curso)
plot(xlikgroup5f, type = "bar", centered = T)
ELN
#subset to remove empty years
subdata[,c(51:72)] <- lapply(subdata[,c(51:72)], function(x) factor(x,
labels = c("Not at all","Disagree","OK","Agree","Completely Agree"))
)
eln <- subset(subdata[,c(1,51:72)][subdata$Curso==2019|subdata$Curso==2020|subdata$Curso==2021|subdata$Curso==2022,])
xlikgroup5g = likert(eln[2:23], grouping = eln$Curso)
plot(xlikgroup5g, type = "bar", centered = T, title="ELN")
Session Info
sessionInfo()## R version 4.2.2 (2022-10-31)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.1.0 reshape2_1.4.4 heatmaply_1.4.2 viridis_0.6.2
## [5] viridisLite_0.4.1 plotly_4.10.1 ggpubr_0.5.0 likert_1.3.5
## [9] xtable_1.8-4 corrplot_0.92 kableExtra_1.3.4 data.table_1.14.6
## [13] ggplot2_3.4.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.4 sass_0.4.5 tidyr_1.3.0 jsonlite_1.8.4
## [5] foreach_1.5.2 carData_3.0-5 bslib_0.4.2 assertthat_0.2.1
## [9] highr_0.10 yaml_2.3.7 pillar_1.8.1 backports_1.4.1
## [13] lattice_0.20-45 glue_1.6.2 digest_0.6.31 RColorBrewer_1.1-3
## [17] ggsignif_0.6.4 rvest_1.0.3 colorspace_2.1-0 htmltools_0.5.4
## [21] plyr_1.8.8 psych_2.2.9 pkgconfig_2.0.3 broom_1.0.3
## [25] purrr_1.0.1 scales_1.2.1 webshot_0.5.4 svglite_2.1.1
## [29] tibble_3.1.8 farver_2.1.1 generics_0.1.3 car_3.1-1
## [33] cachem_1.0.6 withr_2.5.0 lazyeval_0.2.2 cli_3.6.0
## [37] mnormt_2.1.1 magrittr_2.0.3 evaluate_0.20 fansi_1.0.4
## [41] nlme_3.1-161 rstatix_0.7.2 xml2_1.3.3 tools_4.2.2
## [45] registry_0.5-1 lifecycle_1.0.3 stringr_1.5.0 munsell_0.5.0
## [49] ggsci_2.9 compiler_4.2.2 jquerylib_0.1.4 ca_0.71.1
## [53] systemfonts_1.0.4 rlang_1.0.6 grid_4.2.2 iterators_1.0.14
## [57] rstudioapi_0.14 htmlwidgets_1.6.1 labeling_0.4.2 rmarkdown_2.20
## [61] gtable_0.3.1 codetools_0.2-18 abind_1.4-5 TSP_1.2-2
## [65] R6_2.5.1 seriation_1.4.1 gridExtra_2.3 knitr_1.42
## [69] fastmap_1.1.0 utf8_1.2.2 dendextend_1.16.0 stringi_1.7.12
## [73] parallel_4.2.2 Rcpp_1.0.10 vctrs_0.5.2 tidyselect_1.2.0
## [77] xfun_0.36